Credit policies for clean and renewable energy businesses play a crucial role in supporting carbon neutrality efforts to combat climate change. Clustering the credit capacity of these companies to prioritize lending is essential given the limited capital available. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are two robust machine learning algorithms for addressing complex clustering problems. Additionally, hyperparameter selection within these models is effectively enhanced through the support of a robust heuristic optimization algorithm, Particle Swarm Optimization (PSO). To leverage the strength of these advanced machine learning techniques, this paper aims to develop SVM and ANN models, optimized with the PSO, for the clustering problem of green credit capacity in the renewable energy industry. The results show low Mean Square Error (MSE) values for both models, indicating high clustering accuracy. The credit capabilities of wind energy, clean fuel, and biomass pellet companies are illustrated in quadrant charts, providing stakeholders with a clear view to adjust their credit strategies. This helps ensure the efficient operation of banking green credit policies.

Clustering technics, like k-means and its extended version, fuzzy c-means clustering (FCM) are useful tools for identifying typical behaviours based on various attitudes and responses to well-formulated questionnaires, such as among forensic populations. As more or less standard questionnaires for analyzing aggressive attitudes do exist in the literature, the application of these clustering methods seems to be rather straightforward. Especially, fuzzy clustering may lead to new recognitions, as human behaviour and communication are full of uncertainties, which often do not have a probabilistic nature. In this paper, the cluster analysis of a closed forensic (inmate) population will be presented. The goal of this study was by applying fuzzy c-means clustering to facilitate the wider possibilities of analysis of aggressive behaviour which is treated as a heterogeneous construct resulting in two main phenotypes, premeditated and impulsive aggression. Understanding motives of aggression helps reconstruct possible events, sequences of events and scenarios related to a certain crime, and ultimately, to prevent further crimes from happening.

Islamic banking is one of the fastest-growing sectors of the financial industry. Several works have been written in this field, but none attempt to learn the entire Islamic banking and financial system. Furthermore, the study could not locate any publications investigating the conceptual and intellectual foundations of this emerging field of inquiry. The current study uses bibliometric methodologies to assess the current state of Islamic banking, financial research, and the upcoming trends. For the people who choose interest-free investments, the current research examines a conceptual research context on Islamic banking and finance at various planning and decision-making stages. One thousand research studies appearing in scholarly journals between 2005 and 2023 were reviewed for the purpose. In order to examine the works on Islamic banking and finance, bibliometric techniques were used, including analysis of citation network, content, co-citation, keyword, and publishing trends. By suggesting thirteen clusters, to enhance research on Islamic banking and finance to help interest-free investors learn more, the goal of the research is to promote the body of knowledge. The field of Islamic banking and finance has grown from a young lot to a prominent teaching and research tool. Investigating and identifying current research trends in this area is crucial. As institutions and society are placing more emphasis on Islamic banking to raise individual citizens’ responsibilities in developing interest-free investing strategies, the findings are crucial to the community of interest-free financiers. Further research urges with the studies not restricted to a thousand researches only.